def learn(self):
"""
What the function does:
* read dev-set
* for file in dev-set:
* for instance in file:
* res = extract(instance)
* for tok in res:
* cand = Candidate(res) # more complex than this, actually
* if(is_effective_candidate(cand)):
* self.candidates.append(cand)
"""
import glob
import os
import operator
from citation_extractor.Utils import IO
for infile in glob.glob(os.path.join(self.dev_set, '*.iob')):
instances = IO.file_to_instances(infile)
string_instances = [[tok[0] for tok in i]for i in instances]
results = self.classifier.extract([string_instances])
for n,r in enumerate(results):
for tok in r:
probs = [(tag,tok["probs"][tag]["prob"]) for tag in tok["probs"].keys()] # extract the probabilities for each tag
probs.sort(key=lambda tup: tup[1],reverse=True)
self.logger.debug(probs)
cand = Candidate(tok["token"],"%s#%i"%(infile,n),probs[:2]) # just the 2 top most likely tags are considered
if(self.is_effective_candidate(cand)):
self.candidates.append(cand)
self.token_count+=1
self.candidates.sort(key=operator.attrgetter('ci_score'),reverse=True)
return self.candidates
def __init__(self,
extractors,
iob_directories=[],
iob_file=None,
label_index=-1):
"""
Args:
extractors:
the list of canonical citation extractors to evaluate
iob_test_file:
the file in IOB format to be used for testing and evaluating the extactors
"""
# read the test instances from a list of directories containing the test data
import logging
self.logger = logging.getLogger("CREX.SIMPLEVAL")
if (iob_file is None):
self.logger.debug(iob_directories)
data = []
for directory in iob_directories:
data += IO.read_iob_files(directory, ".txt")
self.test_instances = data
else:
self.test_instances = IO.file_to_instances(iob_file)
self.logger.debug("Found %i instances for test" %
len(self.test_instances))
self.extractors = extractors
self.output = {}
self.error_matrix = None
self.label_index = label_index
return
def __init__(self,extractors,iob_directories=[],iob_file=None,label_index=-1):
"""
Args:
extractors:
the list of canonical citation extractors to evaluate
iob_test_file:
the file in IOB format to be used for testing and evaluating the extactors
"""
# read the test instances from a list of directories containing the test data
import logging
self.logger = logging.getLogger("CREX.SIMPLEVAL")
if(iob_file is None):
self.logger.debug(iob_directories)
data = []
for directory in iob_directories:
data += IO.read_iob_files(directory,".txt")
self.test_instances = data
else:
self.test_instances = IO.file_to_instances(iob_file)
self.logger.debug("Found %i instances for test"%len(self.test_instances))
self.extractors = extractors
self.output = {}
self.error_matrix = None
self.label_index = label_index
return
def tag_candidates(settings):
import glob
import os
import codecs
from citation_extractor.Utils import IO
from citation_extractor.core import citation_extractor
extractor = citation_extractor(settings)
for infile in glob.glob(os.path.join(settings.CANDIDATES_DIR,
'*.iob')):
print "processing %s" % infile
instances = IO.file_to_instances(infile)
string_instances = [[tok[0] for tok in i] for i in instances]
results = extractor.extract([string_instances])
out_dir = settings.OUT_DIR
out_fname = "%s%s" % (out_dir, os.path.basename(infile))
file = codecs.open(out_fname, 'w', encoding="utf-8")
instances = [
"\n".join([
"%s\t%s" % (t["token"].decode("utf-8"), t["label"])
for t in r
]) for r in results
]
file.write("\n\n".join(instances))
file.close()
print "output written to %s" % out_fname
def main():
import argparse
parser = argparse.ArgumentParser(description="")
parser.add_argument("input", type=str, help="IOB input file")
parser.add_argument("--standoff-dir",
help="Stand-off directory",
type=str,
required=True)
parser.add_argument("--output-dir",
help="IOB output file",
type=str,
required=True)
args = parser.parse_args()
print >> sys.stderr, "IOB Input:", args.input
print >> sys.stderr, "Stand-off input folder: ", args.standoff_dir
print >> sys.stderr, "IOB output dir:", args.output_dir
fname = os.path.split(args.input)[1].split(".")[0]
# read the correspondant .ann file with stand-off annotation
so_entities, so_relations, so_annotations = read_ann_file(
"%s.txt" % fname, args.standoff_dir)
# extract for each token the start and end
sentences = process(args.input)
token_start_end = get_start_end(sentences)
# read IOB from file
iob_data = IO.file_to_instances(args.input)
# make sure that data is consistent
assert [len(sentence) for sentence in iob_data
] == [len(sentence) for sentence in token_start_end]
so_entities = [(so_entities[ent][1], so_entities[ent][0],
int(so_entities[ent][2]), int(so_entities[ent][3]))
for ent in so_entities.keys()]
updated_iob_instances = update(token_start_end, iob_data, so_entities)
try:
destination = "%s%s.txt" % (args.output_dir, fname)
IO.write_iob_file(updated_iob_instances, destination)
print >> sys.stderr, "IOB output written to \'%s\'" % destination
except Exception, e:
print >> sys.stderr, "Writing ouput to \'%s\' failed with error \'%s\'" % (
destination, e)
def do_ner(doc_id,inp_dir,interm_dir,out_dir,extractor,so2iob_script):
# TODO:
# wrap with a try/except/finally
# return doc_id and a boolean
from citation_extractor.Utils import IO
try:
data = IO.file_to_instances("%s%s"%(inp_dir,doc_id))
postags = [[("z_POS",token[1]) for token in instance] for instance in data if len(instance)>0]
instances = [[token[0] for token in instance] for instance in data if len(instance)>0]
result = extractor.extract(instances,postags)
output = [[(res[n]["token"].decode('utf-8'), postags[i][n][1], res[n]["label"]) for n,d_res in enumerate(res)] for i,res in enumerate(result)]
out_fname = "%s%s"%(interm_dir,doc_id)
IO.write_iob_file(output,out_fname)
logger.info("Output successfully written to file \"%s\""%out_fname)
tostandoff(out_fname,out_dir,so2iob_script)
return (doc_id,True)
except Exception, e:
logger.error("The NER of document %s failed with error \"%s\""%(doc_id,e))
return (doc_id,False)
def tag_candidates(settings):
import glob
import os
import codecs
from citation_extractor.Utils import IO
from citation_extractor.core import citation_extractor
extractor = citation_extractor(settings)
for infile in glob.glob( os.path.join(settings.CANDIDATES_DIR, '*.iob') ):
print "processing %s"%infile
instances = IO.file_to_instances(infile)
string_instances = [[tok[0] for tok in i]for i in instances]
results = extractor.extract([string_instances])
out_dir = settings.OUT_DIR
out_fname = "%s%s"%(out_dir,os.path.basename(infile))
file = codecs.open(out_fname, 'w',encoding="utf-8")
instances = ["\n".join(["%s\t%s"%(t["token"].decode("utf-8"),t["label"]) for t in r]) for r in results]
file.write("\n\n".join(instances))
file.close()
print "output written to %s"%out_fname
def do_ner(doc_id, inp_dir, interm_dir, out_dir, extractor, so2iob_script):
# TODO:
# wrap with a try/except/finally
# return doc_id and a boolean
from citation_extractor.Utils import IO
try:
data = IO.file_to_instances("%s%s" % (inp_dir, doc_id))
postags = [[("z_POS", token[1]) for token in instance]
for instance in data if len(instance) > 0]
instances = [[token[0] for token in instance] for instance in data
if len(instance) > 0]
result = extractor.extract(instances, postags)
output = [[(res[n]["token"].decode('utf-8'), postags[i][n][1],
res[n]["label"]) for n, d_res in enumerate(res)]
for i, res in enumerate(result)]
out_fname = "%s%s" % (interm_dir, doc_id)
IO.write_iob_file(output, out_fname)
logger.info("Output successfully written to file \"%s\"" % out_fname)
tostandoff(out_fname, out_dir, so2iob_script)
return (doc_id, True)
except Exception, e:
logger.error("The NER of document %s failed with error \"%s\"" %
(doc_id, e))
return (doc_id, False)
def learn(self):
"""
What the function does:
* read dev-set
* for file in dev-set:
* for instance in file:
* res = extract(instance)
* for tok in res:
* cand = Candidate(res) # more complex than this, actually
* if(is_effective_candidate(cand)):
* self.candidates.append(cand)
"""
import glob
import os
import operator
from citation_extractor.Utils import IO
for infile in glob.glob(os.path.join(self.dev_set, '*.iob')):
instances = IO.file_to_instances(infile)
string_instances = [[tok[0] for tok in i] for i in instances]
results = self.classifier.extract([string_instances])
for n, r in enumerate(results):
for tok in r:
probs = [(tag, tok["probs"][tag]["prob"])
for tag in tok["probs"].keys()
] # extract the probabilities for each tag
probs.sort(key=lambda tup: tup[1], reverse=True)
self.logger.debug(probs)
cand = Candidate(
tok["token"], "%s#%i" % (infile, n), probs[:2]
) # just the 2 top most likely tags are considered
if (self.is_effective_candidate(cand)):
self.candidates.append(cand)
self.token_count += 1
self.candidates.sort(key=operator.attrgetter('ci_score'), reverse=True)
return self.candidates